# A Ternary Bi-Directional LSTM Classification for Brain Activation Pattern Recognition Using fNIRS

arXiv: https://arxiv.org/abs/2101.05892 · HTML: https://mlsanity.com/paper/2101.05892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 14 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Reducing Overfitting in Deep Networks by Decorrelating Representations](https://mlsanity.com/paper/1511.06068/) (2015)

## Abstract (arXiv)

Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost method used to study the brain's blood flow pattern. Such patterns can enable us to classify performed by a subject. In recent research, most classification systems use traditional machine learning algorithms for the classification of tasks. These methods, which are easier to implement, usually suffer from low accuracy. Further, a complex pre-processing phase is required for data preparation before implementing traditional machine learning methods. The proposed system uses a Bi-Directional LSTM based deep learning architecture for task classification, including mental arithmetic, motor imagery, and idle state using fNIRS data. Further, this system will require less pre-processing than the traditional approach, saving time and computational resources while obtaining an accuracy of 81.48%, which is considerably higher than the accuracy obtained using conventional machine learning algorithms for the same data set.